{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MeterGroup, ElecMeter, selection and basic statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All NILM datasets consists of various groupings of electricity meters.  We can group the meters by house.  Or by the type of appliance they are directly connected to.  Or by sample rate.  Or by whether the meter is a whole-house \"site meter\" or an appliance-level submeter, or a circuit-level submeter.\n",
    "\n",
    "In NILMTK, one of the key classes is `MeterGroup` which stores a list of `meters` and allows us to select a subset of meters, aggregate power from all meters and many other functions.\n",
    "\n",
    "When we first open a `DataSet`, NILMTK creates several `MeterGroup` objects.  There's `nilmtk.global_meter_group` which holds every meter currently loaded (including from multiple datasets if you have opened more than one dataset).  There is also one `MeterGroup` per building (which live in the `Building.elec` attribute).  We also nested `MeterGroups` for aggregating together split-phase mains, 3-phase mains and dual-supply (240 volt) appliances in North American and Canadan datasets.  For example, here is the `MeterGroup` for building 1 in REDD:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**NOTE**: If you are on Windows, remember to escape the back-slashes, use forward-slashs, or use raw-strings when passing paths in Python, e.g. one of the following would work:\n",
    "\n",
    "```python\n",
    "redd = DataSet('c:\\\\data\\\\REDD\\\\redd.h5')\n",
    "redd = DataSet('c:/data/REDD/redd.h5')\n",
    "redd = DataSet(r'c:\\data\\REDD\\redd.h5')\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MeterGroup(meters=\n",
       "  ElecMeter(instance=1, building=1, dataset='REDD', site_meter, appliances=[])\n",
       "  ElecMeter(instance=2, building=1, dataset='REDD', site_meter, appliances=[])\n",
       "  ElecMeter(instance=5, building=1, dataset='REDD', appliances=[Appliance(type='fridge', instance=1)])\n",
       "  ElecMeter(instance=6, building=1, dataset='REDD', appliances=[Appliance(type='dish washer', instance=1)])\n",
       "  ElecMeter(instance=7, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=1)])\n",
       "  ElecMeter(instance=8, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=2)])\n",
       "  ElecMeter(instance=9, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=1)])\n",
       "  ElecMeter(instance=11, building=1, dataset='REDD', appliances=[Appliance(type='microwave', instance=1)])\n",
       "  ElecMeter(instance=12, building=1, dataset='REDD', appliances=[Appliance(type='unknown', instance=1)])\n",
       "  ElecMeter(instance=13, building=1, dataset='REDD', appliances=[Appliance(type='electric space heater', instance=1)])\n",
       "  ElecMeter(instance=14, building=1, dataset='REDD', appliances=[Appliance(type='electric stove', instance=1)])\n",
       "  ElecMeter(instance=15, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=3)])\n",
       "  ElecMeter(instance=16, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=4)])\n",
       "  ElecMeter(instance=17, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=2)])\n",
       "  ElecMeter(instance=18, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=3)])\n",
       "  ElecMeter(instance=19, building=1, dataset='REDD', appliances=[Appliance(type='unknown', instance=2)])\n",
       "  MeterGroup(meters=\n",
       "    ElecMeter(instance=3, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
       "    ElecMeter(instance=4, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
       "  )\n",
       "  MeterGroup(meters=\n",
       "    ElecMeter(instance=10, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
       "    ElecMeter(instance=20, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from matplotlib import rcParams\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "rcParams['figure.figsize'] = (13, 6)\n",
    "plt.style.use('ggplot')\n",
    "from nilmtk import DataSet\n",
    "\n",
    "\n",
    "redd = DataSet('/data/REDD/redd.h5')\n",
    "elec = redd.buildings[1].elec\n",
    "elec"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that there are two nested `MeterGroups`: one for the electric oven, and one for the washer dryer (both of which are 240 volt appliances and have two meters per appliance):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[MeterGroup(meters=\n",
       "   ElecMeter(instance=3, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
       "   ElecMeter(instance=4, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
       " ), MeterGroup(meters=\n",
       "   ElecMeter(instance=10, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
       "   ElecMeter(instance=20, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
       " )]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elec.nested_metergroups()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Putting these meters into a `MeterGroup` allows us to easily sum together the power demand recorded by both meters to get the total power demand for the entire appliance (but it's also very easy to see the individual meter power demand too)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can easily get a MeterGroup of either the submeters or the mains:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MeterGroup(meters=\n",
       "  ElecMeter(instance=1, building=1, dataset='REDD', site_meter, appliances=[])\n",
       "  ElecMeter(instance=2, building=1, dataset='REDD', site_meter, appliances=[])\n",
       ")"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elec.mains()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can easily get the power data for both mains meters summed together:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading data for meter ElecMeterID(instance=2, building=1, dataset='REDD')     \n",
      "Done loading data all meters for this chunk.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2011-04-18 09:22:09-04:00    342.820007\n",
       "2011-04-18 09:22:10-04:00    344.559998\n",
       "2011-04-18 09:22:11-04:00    345.140015\n",
       "2011-04-18 09:22:12-04:00    341.679993\n",
       "2011-04-18 09:22:13-04:00    341.029999\n",
       "Name: (power, apparent), dtype: float32"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elec.mains().power_series_all_data().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MeterGroup(meters=\n",
       "  ElecMeter(instance=5, building=1, dataset='REDD', appliances=[Appliance(type='fridge', instance=1)])\n",
       "  ElecMeter(instance=6, building=1, dataset='REDD', appliances=[Appliance(type='dish washer', instance=1)])\n",
       "  ElecMeter(instance=7, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=1)])\n",
       "  ElecMeter(instance=8, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=2)])\n",
       "  ElecMeter(instance=9, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=1)])\n",
       "  ElecMeter(instance=11, building=1, dataset='REDD', appliances=[Appliance(type='microwave', instance=1)])\n",
       "  ElecMeter(instance=12, building=1, dataset='REDD', appliances=[Appliance(type='unknown', instance=1)])\n",
       "  ElecMeter(instance=13, building=1, dataset='REDD', appliances=[Appliance(type='electric space heater', instance=1)])\n",
       "  ElecMeter(instance=14, building=1, dataset='REDD', appliances=[Appliance(type='electric stove', instance=1)])\n",
       "  ElecMeter(instance=15, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=3)])\n",
       "  ElecMeter(instance=16, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=4)])\n",
       "  ElecMeter(instance=17, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=2)])\n",
       "  ElecMeter(instance=18, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=3)])\n",
       "  ElecMeter(instance=19, building=1, dataset='REDD', appliances=[Appliance(type='unknown', instance=2)])\n",
       "  MeterGroup(meters=\n",
       "    ElecMeter(instance=3, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
       "    ElecMeter(instance=4, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
       "  )\n",
       "  MeterGroup(meters=\n",
       "    ElecMeter(instance=10, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
       "    ElecMeter(instance=20, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elec.submeters()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stats for MeterGroups"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Proportion of energy submetered"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's work out the proportion of energy submetered in REDD building 1:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running MeterGroup.proportion_of_energy_submetered...\n",
      "Calculating total_energy for ElecMeterID(instance=2, building=1, dataset='REDD') ...   "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/nilmtk/nilmtk/metergroup.py:921: UserWarning: As a quick implementation we only get Good Sections from the first meter in the meter group.  We should really return the intersection of the good sections for all meters.  This will be fixed...\n",
      "  warn(\"As a quick implementation we only get Good Sections from\"\n",
      "/nilmtk/nilmtk/electric.py:307: UserWarning: No shared AC types.  Using 'active' for submeter and 'apparent' for other.\n",
      "  \" and '{:s}' for other.\".format(ac_type, other_ac_type))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating total_energy for ElecMeterID(instance=2, building=1, dataset='REDD') ...    ...    total_energy for ElecMeterID(instance=1, building=1, dataset='REDD') ...    total_energy for ElecMeterID(instance=1, building=1, dataset='REDD') ...    total_energy for ElecMeterID(instance=2, building=1, dataset='REDD') ...   "
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.75990318508883459"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elec.proportion_of_energy_submetered()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that NILMTK has raised a warning that Mains uses a different type of power measurement than all the submeters, so it's not an entirely accurate comparison.  Which raises the question: which type of power measurements are used for the mains and submeters?  Let's find out..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Active, apparent and reactive power"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "mains = elec.mains()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['apparent']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mains.available_ac_types('power')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['active']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elec.submeters().available_ac_types('power')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading data for meter ElecMeterID(instance=4, building=1, dataset='REDD')      dataset='REDD'), ElecMeterID(instance=4, building=1, dataset='REDD')))     \n",
      "Done loading data all meters for this chunk.\n",
      "Loading data for meter ElecMeterID(instance=20, building=1, dataset='REDD')      dataset='REDD'), ElecMeterID(instance=20, building=1, dataset='REDD')))     \n",
      "Done loading data all meters for this chunk.\n",
      "\n",
      "Done loading data all meters for this chunk.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[physical_quantity          power            \n",
       " type                      active    apparent\n",
       " 2011-04-18 09:22:09-04:00    NaN  342.820007\n",
       " 2011-04-18 09:22:12-04:00    NaN  341.679993\n",
       " 2011-04-18 09:22:15-04:00  261.0  342.209991\n",
       " 2011-04-18 09:22:18-04:00  261.0  341.809998\n",
       " 2011-04-18 09:22:21-04:00  262.0  341.279999\n",
       " 2011-04-18 09:22:24-04:00  262.0  341.859985\n",
       " 2011-04-18 09:22:27-04:00  261.0  341.539978\n",
       " 2011-04-18 09:22:30-04:00  260.0  341.660004\n",
       " 2011-04-18 09:22:33-04:00  260.0  341.839996\n",
       " 2011-04-18 09:22:36-04:00  260.0  343.570007\n",
       " 2011-04-18 09:22:39-04:00  262.0  344.690002\n",
       " 2011-04-18 09:22:42-04:00  266.0  345.250000\n",
       " 2011-04-18 09:22:45-04:00  260.0  342.709991\n",
       " 2011-04-18 09:22:48-04:00  261.0  341.959991\n",
       " 2011-04-18 09:22:51-04:00  267.0  346.459991\n",
       " 2011-04-18 09:22:54-04:00  262.0  340.330017\n",
       " 2011-04-18 09:22:57-04:00  262.0  344.330017\n",
       " 2011-04-18 09:23:00-04:00  264.0  344.470001\n",
       " 2011-04-18 09:23:03-04:00  267.0  345.299988\n",
       " 2011-04-18 09:23:06-04:00  266.0  343.769989\n",
       " 2011-04-18 09:23:09-04:00  260.0  344.690002\n",
       " 2011-04-18 09:23:12-04:00  260.0  344.109985\n",
       " 2011-04-18 09:23:15-04:00  260.0  345.769989\n",
       " 2011-04-18 09:23:18-04:00  260.0  345.640015\n",
       " 2011-04-18 09:23:21-04:00  260.0  344.330017\n",
       " 2011-04-18 09:23:24-04:00  264.0  346.470001\n",
       " 2011-04-18 09:23:27-04:00  264.0  344.729980\n",
       " 2011-04-18 09:23:30-04:00  263.0  346.390015\n",
       " 2011-04-18 09:23:33-04:00  266.0  344.459991\n",
       " 2011-04-18 09:23:36-04:00  267.0  346.850006\n",
       " ...                          ...         ...\n",
       " 2011-05-24 15:55:33-04:00  246.0  275.369995\n",
       " 2011-05-24 15:55:36-04:00  246.0  277.339996\n",
       " 2011-05-24 15:55:39-04:00  247.0  277.399994\n",
       " 2011-05-24 15:55:42-04:00  240.0  274.440002\n",
       " 2011-05-24 15:55:45-04:00  240.0  275.059998\n",
       " 2011-05-24 15:55:48-04:00  240.0  273.660004\n",
       " 2011-05-24 15:55:51-04:00  244.0  276.980011\n",
       " 2011-05-24 15:55:54-04:00  246.0  277.619995\n",
       " 2011-05-24 15:55:57-04:00  242.0  274.650024\n",
       " 2011-05-24 15:56:00-04:00  240.0  273.910004\n",
       " 2011-05-24 15:56:03-04:00  243.0  273.489990\n",
       " 2011-05-24 15:56:06-04:00  241.0  273.140015\n",
       " 2011-05-24 15:56:09-04:00  241.0  273.369995\n",
       " 2011-05-24 15:56:12-04:00  242.0  273.389984\n",
       " 2011-05-24 15:56:15-04:00  245.0  273.630005\n",
       " 2011-05-24 15:56:18-04:00  243.0  273.460022\n",
       " 2011-05-24 15:56:21-04:00  242.0  273.799988\n",
       " 2011-05-24 15:56:24-04:00  240.0  274.270020\n",
       " 2011-05-24 15:56:27-04:00  243.0  274.070007\n",
       " 2011-05-24 15:56:30-04:00  242.0  274.750000\n",
       " 2011-05-24 15:56:33-04:00  242.0  273.410004\n",
       " 2011-05-24 15:56:36-04:00    NaN  273.089996\n",
       " 2011-05-24 15:56:39-04:00    NaN  277.770020\n",
       " 2011-05-24 15:56:42-04:00    NaN  273.649994\n",
       " 2011-05-24 15:56:45-04:00    NaN  272.709991\n",
       " 2011-05-24 15:56:48-04:00    NaN  273.670013\n",
       " 2011-05-24 15:56:51-04:00    NaN  273.760010\n",
       " 2011-05-24 15:56:54-04:00    NaN  273.619995\n",
       " 2011-05-24 15:56:57-04:00    NaN  273.649994\n",
       " 2011-05-24 15:57:00-04:00    NaN  273.390015\n",
       " \n",
       " [1044698 rows x 2 columns]]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(elec.load())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Total Energy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating total_energy for ElecMeterID(instance=2, building=1, dataset='REDD') ...   "
     ]
    },
    {
     "data": {
      "text/plain": [
       "apparent    167.766283\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elec.mains().total_energy() # returns kWh"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Energy per submeter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15/16 MeterGroup(meters==19, building=1, dataset='REDD', appliances=[Appliance(type='unknown', instance=2)])e=1)])ce=1)])\n",
      "  ElecMeter(instance=3, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
      "  ElecMeter(instance=4, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
      "16/16 MeterGroup(meters= for ElecMeterID(instance=4, building=1, dataset='REDD') ...   \n",
      "  ElecMeter(instance=10, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
      "  ElecMeter(instance=20, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
      "Calculating total_energy for ElecMeterID(instance=20, building=1, dataset='REDD') ...   "
     ]
    },
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>(5, 1, REDD)</th>\n",
       "      <th>(6, 1, REDD)</th>\n",
       "      <th>(7, 1, REDD)</th>\n",
       "      <th>(8, 1, REDD)</th>\n",
       "      <th>(9, 1, REDD)</th>\n",
       "      <th>(11, 1, REDD)</th>\n",
       "      <th>(12, 1, REDD)</th>\n",
       "      <th>(13, 1, REDD)</th>\n",
       "      <th>(14, 1, REDD)</th>\n",
       "      <th>(15, 1, REDD)</th>\n",
       "      <th>(16, 1, REDD)</th>\n",
       "      <th>(17, 1, REDD)</th>\n",
       "      <th>(18, 1, REDD)</th>\n",
       "      <th>(19, 1, REDD)</th>\n",
       "      <th>(((3, 1, REDD), (4, 1, REDD)),)</th>\n",
       "      <th>(((10, 1, REDD), (20, 1, REDD)),)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>active</th>\n",
       "      <td>44.750925</td>\n",
       "      <td>19.920875</td>\n",
       "      <td>16.786282</td>\n",
       "      <td>22.939649</td>\n",
       "      <td>30.734511</td>\n",
       "      <td>16.890262</td>\n",
       "      <td>5.221226</td>\n",
       "      <td>0.096302</td>\n",
       "      <td>0.411592</td>\n",
       "      <td>4.507334</td>\n",
       "      <td>2.256583</td>\n",
       "      <td>18.288595</td>\n",
       "      <td>11.811224</td>\n",
       "      <td>0.000085</td>\n",
       "      <td>8.81796</td>\n",
       "      <td>32.614809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>apparent</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reactive</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          (5, 1, REDD)  (6, 1, REDD)  (7, 1, REDD)  (8, 1, REDD)  \\\n",
       "active       44.750925     19.920875     16.786282     22.939649   \n",
       "apparent           NaN           NaN           NaN           NaN   \n",
       "reactive           NaN           NaN           NaN           NaN   \n",
       "\n",
       "          (9, 1, REDD)  (11, 1, REDD)  (12, 1, REDD)  (13, 1, REDD)  \\\n",
       "active       30.734511      16.890262       5.221226       0.096302   \n",
       "apparent           NaN            NaN            NaN            NaN   \n",
       "reactive           NaN            NaN            NaN            NaN   \n",
       "\n",
       "          (14, 1, REDD)  (15, 1, REDD)  (16, 1, REDD)  (17, 1, REDD)  \\\n",
       "active         0.411592       4.507334       2.256583      18.288595   \n",
       "apparent            NaN            NaN            NaN            NaN   \n",
       "reactive            NaN            NaN            NaN            NaN   \n",
       "\n",
       "          (18, 1, REDD)  (19, 1, REDD)  (((3, 1, REDD), (4, 1, REDD)),)  \\\n",
       "active        11.811224       0.000085                          8.81796   \n",
       "apparent            NaN            NaN                              NaN   \n",
       "reactive            NaN            NaN                              NaN   \n",
       "\n",
       "          (((10, 1, REDD), (20, 1, REDD)),)  \n",
       "active                            32.614809  \n",
       "apparent                                NaN  \n",
       "reactive                                NaN  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "energy_per_meter = elec.submeters().energy_per_meter() # kWh, again\n",
    "energy_per_meter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "column headings are the ElecMeter instance numbers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function `fraction_per_meter` does the same thing as `energy_per_submeter` but returns the fraction of energy per meter."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Select meters on the basis of their energy consumption"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's make a new MeterGroup which only contains the ElecMeters which used more than 20 kWh:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 1, REDD)                         44.750925\n",
       "(8, 1, REDD)                         22.939649\n",
       "(9, 1, REDD)                         30.734511\n",
       "(((10, 1, REDD), (20, 1, REDD)),)    32.614809\n",
       "Name: active, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# energy_per_meter is a DataFrame where each row is a \n",
    "# power type ('active', 'reactive' or 'apparent').\n",
    "# All appliance meters in REDD are record 'active' so just select\n",
    "# the 'active' row:\n",
    "energy_per_meter = energy_per_meter.loc['active']\n",
    "more_than_20 = energy_per_meter[energy_per_meter > 20]\n",
    "more_than_20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([                       (5, 1, 'REDD'),\n",
       "                              (8, 1, 'REDD'),\n",
       "                              (9, 1, 'REDD'),\n",
       "       (((10, 1, 'REDD'), (20, 1, 'REDD')),)],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "instances = more_than_20.index\n",
    "instances"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Draw wiring diagram"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can get the wiring diagram for the MeterGroup:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<networkx.classes.digraph.DiGraph at 0x1db4321bba8>,\n",
       " <matplotlib.axes._axes.Axes at 0x1db3b6f7470>)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8p1evXqxcuZKAgICnupw/6+LFi1R5xbjLihUrar65do0ENN3NM88tbAVcevS9M7ANOAjU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\nSUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 936x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "elec.draw_wiring_graph()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's not very pretty but it shows that meters (1,2) (the site meters) are upstream of all other meters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Buildings in REDD have only two levels in their meter hierarchy (mains and submeters).  If there were more than two levels then it might be useful to get only the meters immediately downstream of mains:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MeterGroup(meters=\n",
       "  ElecMeter(instance=5, building=1, dataset='REDD', appliances=[Appliance(type='fridge', instance=1)])\n",
       "  ElecMeter(instance=6, building=1, dataset='REDD', appliances=[Appliance(type='dish washer', instance=1)])\n",
       "  ElecMeter(instance=7, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=1)])\n",
       "  ElecMeter(instance=8, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=2)])\n",
       "  ElecMeter(instance=9, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=1)])\n",
       "  ElecMeter(instance=11, building=1, dataset='REDD', appliances=[Appliance(type='microwave', instance=1)])\n",
       "  ElecMeter(instance=12, building=1, dataset='REDD', appliances=[Appliance(type='unknown', instance=1)])\n",
       "  ElecMeter(instance=13, building=1, dataset='REDD', appliances=[Appliance(type='electric space heater', instance=1)])\n",
       "  ElecMeter(instance=14, building=1, dataset='REDD', appliances=[Appliance(type='electric stove', instance=1)])\n",
       "  ElecMeter(instance=15, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=3)])\n",
       "  ElecMeter(instance=16, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=4)])\n",
       "  ElecMeter(instance=17, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=2)])\n",
       "  ElecMeter(instance=18, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=3)])\n",
       "  ElecMeter(instance=19, building=1, dataset='REDD', appliances=[Appliance(type='unknown', instance=2)])\n",
       "  ElecMeter(instance=3, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
       "  ElecMeter(instance=4, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
       "  ElecMeter(instance=10, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
       "  ElecMeter(instance=20, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
       ")"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elec.meters_directly_downstream_of_mains()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stats and info for individual meters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `ElecMeter` class represents a single electricity meter.  Each `ElecMeter` has a list of associated `Appliance` objects.  `ElecMeter` has many of the same stats methods as `MeterGroup` such as `total_energy` and `available_power_ac_types` and `power_series` and `power_series_all_data`.  We will now explore some more stats functions (many of which are also available on `MeterGroup`)..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "fridge_meter = elec['fridge']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get upstream meter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MeterGroup(meters=\n",
       "  ElecMeter(instance=1, building=1, dataset='REDD', site_meter, appliances=[])\n",
       "  ElecMeter(instance=2, building=1, dataset='REDD', site_meter, appliances=[])\n",
       ")"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fridge_meter.upstream_meter() # happens to be the mains meter group!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Metadata about the class of meter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'description': 'Measures circuit-level power demand.  Comes with 24 CTs. This FAQ page suggests the eMonitor measures real (active) power: http://www.energycircle.com/node/14103  although the REDD readme.txt says all channels record apparent power.\\n',\n",
       " 'manufacturer': 'Powerhouse Dynamics',\n",
       " 'manufacturer_url': 'http://powerhousedynamics.com',\n",
       " 'max_sample_period': 50,\n",
       " 'measurements': [{'lower_limit': 0,\n",
       "   'physical_quantity': 'power',\n",
       "   'type': 'active',\n",
       "   'upper_limit': 5000}],\n",
       " 'model': 'eMonitor',\n",
       " 'sample_period': 3,\n",
       " 'wireless': False}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fridge_meter.device"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dominant appliance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If the metadata specifies that a meter has multiple meters connected to it then one of those can be specified as the 'dominant' appliance, and this appliance can be retrieved with this method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Appliance(type='fridge', instance=1)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fridge_meter.dominant_appliance()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Total energy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "active    44.750925\n",
       "dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fridge_meter.total_energy() # kWh"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get good sections"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we plot the raw power data then we see there is one large gap where, supposedly, the metering system was not working.  (if we were to zoom in then we'd see lots of smaller gaps too):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1db41ee4b00>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PBdOcsGLBNB5nc7ddCIlpLTfLFtbGws81X/D7lC3yIHRzm2WaE1YsmBTgORaNyy+d7Cr5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\n75ds6IolI0qC8Jf/pfSOWCgWKBZLZX2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CajwWNuUD9O9VLVStWBD3I+8zpb5nNmkLIXka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Tj6mYFD7LeI0MgKKUKzQOdo3I5tw61N0mau25MWg8D3H6WjbB25uwX4eqSfOf11+5m7Qin0tJtD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\nQgghhBBC2O2/VyC14lz0O2wAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 936x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fridge_meter.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can automatically identify the 'good sections' (i.e. the sections where every pair of consecutive samples is less than `max_sample_period` specified in the dataset metadata):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "good_sections = fridge_meter.good_sections(full_results=True)\n",
    "# specifying full_results=False would give us a simple list of \n",
    "# TimeFrames.  But we want the full GoodSectionsResults object so we can\n",
    "# plot the good sections..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1db42b52c50>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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du3LlyuMqCgAAaL6p82lDAADgmIR/AABIhPAPAACJEP4BACARwj8AACRC+AcAgEQI/wAA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\n",
      "text/plain": [
       "<Figure size 936x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "good_sections.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The blue chunks show where the data is good.  The white gap is the large gap seen in the raw power data.  There are lots of smaller gaps that we cannot see at this zoom level."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also see the exact sections identified:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[TimeFrame(start='2011-04-18 09:22:13-04:00', end='2011-04-18 14:00:33-04:00', empty=False),\n",
       " TimeFrame(start='2011-04-18 14:03:11-04:00', end='2011-04-19 18:45:09-04:00', empty=False),\n",
       " TimeFrame(start='2011-04-19 20:20:05-04:00', end='2011-04-20 01:54:26-04:00', empty=False),\n",
       " TimeFrame(start='2011-04-20 01:55:33-04:00', end='2011-04-21 06:14:43-04:00', empty=False),\n",
       " TimeFrame(start='2011-04-21 06:17:22-04:00', end='2011-04-21 17:45:02-04:00', empty=False),\n",
       " TimeFrame(start='2011-04-21 19:41:23-04:00', end='2011-04-22 22:46:53-04:00', empty=False),\n",
       " TimeFrame(start='2011-04-22 22:48:31-04:00', end='2011-04-24 03:48:44-04:00', empty=False),\n",
       " TimeFrame(start='2011-04-24 03:52:28-04:00', end='2011-04-27 02:50:14-04:00', empty=False),\n",
       " TimeFrame(start='2011-04-27 02:51:25-04:00', end='2011-04-27 03:17:31-04:00', empty=False),\n",
       " TimeFrame(start='2011-04-27 03:21:19-04:00', end='2011-04-29 23:07:52-04:00', empty=False),\n",
       " TimeFrame(start='2011-04-29 23:10:38-04:00', end='2011-05-01 09:44:36-04:00', empty=False),\n",
       " TimeFrame(start='2011-05-01 09:47:22-04:00', end='2011-05-02 17:04:54-04:00', empty=False),\n",
       " TimeFrame(start='2011-05-02 17:07:31-04:00', end='2011-05-03 17:30:12-04:00', empty=False),\n",
       " TimeFrame(start='2011-05-03 17:32:53-04:00', end='2011-05-03 17:33:17-04:00', empty=False),\n",
       " TimeFrame(start='2011-05-06 10:51:50-04:00', end='2011-05-07 02:38:12-04:00', empty=False),\n",
       " TimeFrame(start='2011-05-07 02:40:47-04:00', end='2011-05-08 13:32:42-04:00', empty=False),\n",
       " TimeFrame(start='2011-05-08 13:33:34-04:00', end='2011-05-11 03:16:14-04:00', empty=False),\n",
       " TimeFrame(start='2011-05-11 03:19:47-04:00', end='2011-05-12 17:48:31-04:00', empty=False),\n",
       " TimeFrame(start='2011-05-12 20:14:27-04:00', end='2011-05-22 01:00:03-04:00', empty=False),\n",
       " TimeFrame(start='2011-05-22 01:01:01-04:00', end='2011-05-22 16:03:54-04:00', empty=False),\n",
       " TimeFrame(start='2011-05-22 16:04:50-04:00', end='2011-05-22 23:38:54-04:00', empty=False),\n",
       " TimeFrame(start='2011-05-22 23:41:39-04:00', end='2011-05-23 09:22:00-04:00', empty=False),\n",
       " TimeFrame(start='2011-05-23 10:31:31-04:00', end='2011-05-24 14:32:01-04:00', empty=False),\n",
       " TimeFrame(start='2011-05-24 15:55:38-04:00', end='2011-05-24 15:56:34-04:00', empty=False)]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "good_sections.combined()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dropout rate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As well as large gaps appearing because the entire system is down, we also get frequent small gaps from wireless sensors dropping data.  This is sometimes called 'dropout'.  The dropout rate is a number between 0 and 1 which specifies the proportion of missing samples.  A dropout rate of 0 means no samples are missing.  A value of 1 would mean all samples are missing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.21922786156570626"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fridge_meter.dropout_rate()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the dropout rate has gone down (which is good!) now that we are ignoring the gaps.  This value is probably more representative of the performance of the wireless system."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Select subgroups of meters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use `ElecMeter.select_using_appliances()` to select a new MeterGroup using an metadata field.  For example, to get all the washer dryers in the whole of the REDD dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MeterGroup(meters=\n",
       "  ElecMeter(instance=10, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
       "  ElecMeter(instance=20, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
       "  ElecMeter(instance=7, building=2, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
       "  ElecMeter(instance=13, building=3, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
       "  ElecMeter(instance=14, building=3, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
       "  ElecMeter(instance=7, building=4, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
       "  ElecMeter(instance=8, building=5, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
       "  ElecMeter(instance=9, building=5, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
       "  ElecMeter(instance=4, building=6, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
       ")"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import nilmtk\n",
    "nilmtk.global_meter_group.select_using_appliances(type='washer dryer')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or select multiple appliance types:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MeterGroup(meters=\n",
       "  ElecMeter(instance=5, building=1, dataset='REDD', appliances=[Appliance(type='fridge', instance=1)])\n",
       "  ElecMeter(instance=11, building=1, dataset='REDD', appliances=[Appliance(type='microwave', instance=1)])\n",
       ")"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elec.select_using_appliances(type=['fridge', 'microwave'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or all appliances in the 'heating' category:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MeterGroup(meters=\n",
       "  ElecMeter(instance=13, building=1, dataset='REDD', appliances=[Appliance(type='electric space heater', instance=1)])\n",
       "  ElecMeter(instance=10, building=3, dataset='REDD', appliances=[Appliance(type='electric furnace', instance=1)])\n",
       "  ElecMeter(instance=4, building=4, dataset='REDD', appliances=[Appliance(type='electric furnace', instance=1)])\n",
       "  ElecMeter(instance=6, building=5, dataset='REDD', appliances=[Appliance(type='electric furnace', instance=1)])\n",
       "  ElecMeter(instance=12, building=5, dataset='REDD', appliances=[Appliance(type='electric space heater', instance=1)])\n",
       "  ElecMeter(instance=13, building=5, dataset='REDD', appliances=[Appliance(type='electric space heater', instance=1)])\n",
       "  ElecMeter(instance=12, building=6, dataset='REDD', appliances=[Appliance(type='electric space heater', instance=1)])\n",
       ")"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nilmtk.global_meter_group.select_using_appliances(category='heating')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or all appliances in building 1 with a single-phase induction motor(!):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MeterGroup(meters=\n",
       "  ElecMeter(instance=5, building=1, dataset='REDD', appliances=[Appliance(type='fridge', instance=1)])\n",
       "  ElecMeter(instance=6, building=1, dataset='REDD', appliances=[Appliance(type='dish washer', instance=1)])\n",
       "  ElecMeter(instance=10, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
       "  ElecMeter(instance=20, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
       ")"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nilmtk.global_meter_group.select_using_appliances(building=1, category='single-phase induction motor')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(NILMTK imports the 'common metadata' from the NILM Metadata project, which includes a wide range of different [category taxonomies](http://nilm-metadata.readthedocs.org/en/latest/central_metadata.html#appliancetype))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MeterGroup(meters=\n",
       "  ElecMeter(instance=7, building=2, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
       "  ElecMeter(instance=10, building=2, dataset='REDD', appliances=[Appliance(type='dish washer', instance=1)])\n",
       "  ElecMeter(instance=11, building=2, dataset='REDD', appliances=[Appliance(type='waste disposal unit', instance=1)])\n",
       ")"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nilmtk.global_meter_group.select_using_appliances(building=2, category='laundry appliances')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Select a group of meters from properties of the meters (not the appliances)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MeterGroup(meters=\n",
       "  ElecMeter(instance=1, building=1, dataset='REDD', site_meter, appliances=[])\n",
       "  ElecMeter(instance=2, building=1, dataset='REDD', site_meter, appliances=[])\n",
       ")"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elec.select(device_model='REDD_whole_house')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MeterGroup(meters=\n",
       "  ElecMeter(instance=5, building=1, dataset='REDD', appliances=[Appliance(type='fridge', instance=1)])\n",
       "  ElecMeter(instance=6, building=1, dataset='REDD', appliances=[Appliance(type='dish washer', instance=1)])\n",
       "  ElecMeter(instance=7, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=1)])\n",
       "  ElecMeter(instance=8, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=2)])\n",
       "  ElecMeter(instance=9, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=1)])\n",
       "  ElecMeter(instance=11, building=1, dataset='REDD', appliances=[Appliance(type='microwave', instance=1)])\n",
       "  ElecMeter(instance=12, building=1, dataset='REDD', appliances=[Appliance(type='unknown', instance=1)])\n",
       "  ElecMeter(instance=13, building=1, dataset='REDD', appliances=[Appliance(type='electric space heater', instance=1)])\n",
       "  ElecMeter(instance=14, building=1, dataset='REDD', appliances=[Appliance(type='electric stove', instance=1)])\n",
       "  ElecMeter(instance=15, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=3)])\n",
       "  ElecMeter(instance=16, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=4)])\n",
       "  ElecMeter(instance=17, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=2)])\n",
       "  ElecMeter(instance=18, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=3)])\n",
       "  ElecMeter(instance=19, building=1, dataset='REDD', appliances=[Appliance(type='unknown', instance=2)])\n",
       "  MeterGroup(meters=\n",
       "    ElecMeter(instance=3, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
       "    ElecMeter(instance=4, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
       "  )\n",
       "  MeterGroup(meters=\n",
       "    ElecMeter(instance=10, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
       "    ElecMeter(instance=20, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elec.select(sample_period=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Select a single meter from a MeterGroup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use `[]` to retrive a single `ElecMeter` from a `MeterGroup`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Search for a meter using appliances connected to each meter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ElecMeter(instance=5, building=1, dataset='REDD', appliances=[Appliance(type='fridge', instance=1)])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elec['fridge']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Appliances are uniquely identified within a building by a `type` (fridge, kettle, television, etc.) and an `instance` number.  If we do not specify an instance number then `ElecMeter` retrieves instance 1 (instance numbering starts from 1).  If you want a different instance then just do this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ElecMeter(instance=17, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=2)])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elec['light', 2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To uniquely identify an appliance in `nilmtk.global_meter_group` then we must specify the dataset name, building instance number, appliance type and appliance instance in a dict:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ElecMeter(instance=5, building=1, dataset='REDD', appliances=[Appliance(type='fridge', instance=1)])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import nilmtk\n",
    "nilmtk.global_meter_group[{'dataset': 'REDD', 'building': 1, 'type': 'fridge', 'instance': 1}]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Search for a meter using details of the ElecMeter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "get ElecMeter with instance = 1:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ElecMeter(instance=1, building=1, dataset='REDD', site_meter, appliances=[])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elec[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Instance numbering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ElecMeter and Appliance instance numbers uniquely identify the meter or appliance type within the *building*, not globally.  To uniquely identify a meter globally, we need three keys:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ElecMeter(instance=8, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=2)])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from nilmtk.elecmeter import ElecMeterID \n",
    "# ElecMeterID is a namedtuple for uniquely identifying each ElecMeter\n",
    "\n",
    "nilmtk.global_meter_group[ElecMeterID(instance=8, building=1, dataset='REDD')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Select nested MeterGroup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also select a single, existing nested MeterGroup.  There are two ways to specify a nested MeterGroup:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MeterGroup(meters=\n",
       "  ElecMeter(instance=3, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
       "  ElecMeter(instance=4, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
       ")"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elec[[ElecMeterID(instance=3, building=1, dataset='REDD'), \n",
    "      ElecMeterID(instance=4, building=1, dataset='REDD')]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MeterGroup(meters=\n",
       "  ElecMeter(instance=3, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
       "  ElecMeter(instance=4, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
       ")"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elec[ElecMeterID(instance=(3,4), building=1, dataset='REDD')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also specify the mains by asking for meter instance 0:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MeterGroup(meters=\n",
       "  ElecMeter(instance=1, building=1, dataset='REDD', site_meter, appliances=[])\n",
       "  ElecMeter(instance=2, building=1, dataset='REDD', site_meter, appliances=[])\n",
       ")"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elec[ElecMeterID(instance=0, building=1, dataset='REDD')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "which is equivalent to elec.mains():"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elec.mains() == elec[ElecMeterID(instance=0, building=1, dataset='REDD')]"
   ]
  }
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